2026-06-03T14:37:36Zhttps://riuma.uma.es/rest/oai/requestoai:riuma.uma.es:10630/281172026-02-03T10:51:52Zcom_10630_2254col_10630_37953
RIUMA. Repositorio Institucional de la Universidad de Málaga
author
Ortiz-García, Andrés
author
Munilla-Fajardo, Jorge
author
Górriz-Sáez, Juan Manuel
author
Ramírez, Javier
2023-11-23T06:54:02Z
2023-11-23T06:54:02Z
2016-08-01
Ortiz, Andrés & Munilla, Jorge & Gorriz, Juan & Ramírez, Javier. (2016). Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer's Disease. International Journal of Neural Systems. 26. 10.1142/S0129065716500258.
https://hdl.handle.net/10630/28117
10.1142/S0129065716500258
Computer Aided Diagnosis (CAD) constitutes an important tool for the early diagnosis of Alzheimer’s Disease (AD), which, in turn, allows the application of treatments that can be simpler and more likely to be effective. This paper explores the construction of classification methods based on deep learning architectures applied on brain regions defined by the Automated Anatomical Labeling (AAL). Gray Matter (GM) images from each brain area have been split into 3D patches according to the regions defined by the AAL atlas and these patches are used to train different deep belief networks. An ensemble of deep belief networks is then composed where the final prediction is determined by a voting scheme. Two deep learning based structures and four different voting schemes are implemented and compared, giving as a result a potent classification architecture where discriminative features are computed in an unsupervised fashion. The resulting method has been evaluated using a large dataset from the Alzheimer’s disease Neuroimaging Initiative (ADNI). Classification results assessed by cross-validation prove that the proposed method is not only valid for differentiate between controls (NC) and AD images, but it also provides good performances when tested for the more challenging case of classifying Mild Cognitive Impairment (MCI) Subjects. In particular, the classification architecture provides accuracy values up to 0.90 and AUC of 0.95 for NC/AD classification, 0.84 and AUC of 0.91 for stable MCI/AD classification and 0.83 and AUC of 0.95 for NC/MCI converters classification.
eng
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Alzheimer, Enfermedad de
Diagnóstico por imagen
Aprendizaje automático (Inteligencia artificial)
Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer’s Disease.
journal article
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